Abstract
Finding discriminative motifs has recently received much attention in biomedical field as such motifs allows us to characterize in distinguishing two different classes of sequences. Although the developed methods function on labeled data, it is common in biomedical applications that the quantity of labeled sequences is limited while a large number of unlabeled sequences is usually available. To overcome this obstacle, this paper presents a proposed semi-supervised learning method that enables the user to exploit unlabeled sequences to enlarge labeled sequence set, leading to improvement of the performance in finding discriminative motifs. The comparative experimental evaluation of the proposed semi-supervised learning shows that it can improve considerably the predictive accuracy of the found motifs.
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References
Lin, T., Murphy, R.F., Bar-Joseph, Z.: Discriminative motif finding for predicting protein subcellular localization. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8(2) (2011)
Kim, J.K., Choi, S.: Probabilistic models for semi-supervised discriminative motif discovery in DNA sequences. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8(5) (2011)
Vens, C., Rosso, M.N., Danchin, E.G.J.: Identifying discriminative classifcation-based motifs in biological sequences. Bioinformatics 27(9), 1231–1238 (2011)
Gao, M., Nettles, R.E., et al.: Chemical genetics strategy identifies an HCV NS5A inhibitor with a potent clinical effect. Nature 465, 953–960 (2010)
Guilou-Guillemette, H.L., Vallet, S., Gaudy-Graffin, C., Payan, C., Pivert, A., Goudeau, A., Lunel-Fabiani, F.: Genetic diversity of the hepatitis C virus: Impact and issues in the antiviral therapy. World Journal of Gastroenterology 13(17), 2416–2426 (2007)
Los Alamos National Laboratory, http://hcv.lanl.gov/
Genbank, http://www.ncbi.nlm.nih.gov/genbank/
Hepatitis Virus Database, http://s2as02.genes.nig.ac.jp/
Ho, T.B., Kawasaki, S., Le, N.T., Kanda, T., Le, N., Takabayashi, K., Yokosuka, O.: Finding HCV NS5A discriminative motifs for assessment of INF/RBV therapy effect. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (2011)
Redhead, E., Bailey, T.L.: Discriminative motif discovery in DNA and protein sequences using the DEME algorithm. BMC Bioinformatics 8 (2007)
Sinha, S.: Discriminative motifs. Journal of Computational Biology 10 (2003)
Zhu, X.: Tutorial on semi-supervised learning - ICML (2007)
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© 2012 Springer-Verlag Berlin Heidelberg
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Le, T.N., Ho, T.B. (2012). A Semi-Supervised Method for Discriminative Motif Finding and Its Application to Hepatitis C Virus Study. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7196. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28487-8_39
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DOI: https://doi.org/10.1007/978-3-642-28487-8_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28486-1
Online ISBN: 978-3-642-28487-8
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